Dynamics of leaf chlorophyll fluorescence parameters can well be tracked by coupling VIS-NIR-SWIR hyperspectral reflectance and light drivers in partial least-squares regression

Chlorophyll alpha fluorescence (ChlF)-derived parameters provide deep insights into the photosynthetic functions of vegetation, and their rapid tracking is of great interest to plant studies. Unfortunately, traditional approaches such as direct measurements or regressive derivation from leaf traits are impossible to capture their dynamics, owing to their high variability under changing light conditions. Recent advances in reflectance spectroscopy address provide rapid and non-destructive estimates of ChlF parameters but lack thorough investigations. In this study, partial least-squares regression (PLSR), coupled with wavelength point and interval selection methods, was attempted to estimate ChlF parameters quickly and non-destructively using the coupled information from leaf spectroscopy, leaf traits, and light conditions recorded in a synchronous dataset covering ChlF parameters, leaf traits, and leaf hyperspectral reflectance under various light conditions. The results showed that PLSR models using only reflectance spectroscopy information predicted ChlF parameters well and maintained effectively across diverse light conditions, with the one coupled with stepwise regression variable selection performing best. Unexpectedly, the coupling of reflectance spectra and leaf traits achieved only marginal improvements. In comparison, the combination of spectral information with light conditions has further improved the estimation of ChlF parameters (R-2: 0.92-0.96, RPIQ: 4.59-7.53) pronouncedly. The results of the present study highlight that the leaf hyperspectral reflectance together with light drivers can efficiently monitor different types of ChlF parameters, strengthening the use of hyperspectral reflectance in plant functions and thus providing valuable information in functional ecology.